1. Field of the Invention
The present invention relates to a data processing system based on the concept of the neural network.
2. Description of the Related Art
The neural network according to a data processing system of this type is structured with layers by preparing a plurality of neuron models 5 (hereinafter called "neuron"), shown in FIG. 1, in a parallel configuration. Neurons in each layer are connected via their respective synapses to all neurons in other adjacent layers, so as to input and output data. According to neuron 5, data O is output in accordance with the compression result between the sum of multiplied input data from outside I1, I2, I3 . . . In by weights W1, W2, W3 . . . Wn and threshold .THETA..
Various compression methods are possible. For example, when the normalization function 1[f] is applied, output data O is expressed as follows: EQU O=1[.SIGMA.Wn.multidot.In-.THETA.] (1)
That is, when .SIGMA.Wn.multidot.In exceed the threshold .THETA., the neuron ignites so that output data O becomes "1"; and when .SIGMA.Wn.multidot.In is smaller than threshold .THETA., output data becomes "0".
A conventional neural network is structured to form neural layers by preparing such neurons 5 in parallel and connecting the above neural layers in series. A neural layer may, for example, comprise 3 layers: an input layer, a middle layer and an output layer. Such a neural layer is proposed by Rosenblatt and described as Perceptrons, in which neurons of each layer have their respective synapses connected to all the neurons of an adjacent layer.
According to the data processing system of this type, it is necessary to prepare a plurality of neurons in an input layer for the processing of input data. However, comparatively few neurons are necessary in an output layer since in practice they simply output the processing result. Conventionally, no specific theory was established with respect to the method for deciding the number of neurons in the middle layer, so that the equal numbers of neurons for all neural layers were normally prepared. Therefore, a problem arose that all neurons are not efficiently used due to the enormous number of neurons.